Method for building language model, speech recognition method and electronic apparatus

ABSTRACT

A method for building a language model, a speech recognition method and an electronic apparatus are provided. The speech recognition method includes the following steps. Phonetic transcriptions of a speech signal are obtained from an acoustic model. Phonetic spellings matching the phonetic transcriptions are obtained according to the phonetic transcriptions and a syllable acoustic lexicon. According to the phonetic spellings, a plurality of text sequences and a plurality of text sequence probabilities are obtained from a language model. Each phonetic spelling is matched to a candidate sentence table; a word probability of each phonetic spelling matching a word in a sentence of the sentence table are obtained; and the word probabilities of the phonetic spellings are calculated so as to obtain the text sequence probabilities. The text sequence corresponding to a largest one of the sequence probabilities is selected as a recognition result of the speech signal.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional application of and claims the prioritybenefit of U.S. application Ser. No. 14/499,261, filed on Sep. 29, 2014,now pending, which claims the priority benefit of China applicationserial no. 201310489580.0, filed on Oct. 18, 2013. The entirety of eachof the above-mentioned patent applications is hereby incorporated byreference herein and made a part of this specification.

BACKGROUND OF THE INVENTION

Field of the Invention

The invention relates to a speech recognition technique, and moreparticularly, relates to a method for building a language model, aspeech recognition method for recognizing speeches of differentlanguages, dialects or pronunciation habits and an electronic apparatusthereof.

Description of Related Art

Speech recognition is no doubt a popular research and business topic.Generally, speech recognition is to extract feature parameters from aninputted speech and then compare the feature parameters with samples inthe database to find and extract the sample that has less dissimilaritywith respect to the inputted speech.

One common method is to collect speech corpus (e.g. recorded humanspeeches) and manually mark the speech corpus (i.e. annotating eachspeech with a corresponding text), and then use the corpus to train anacoustic model and an acoustic lexicon. Therein, the acoustic model andthe acoustic lexicon are trained by utilizing a plurality of speechcorpuses corresponding to a plurality of vocabularies and a plurality ofphonetic transcriptions of the vocabularies marked in a dictionary.Accordingly, data of the speech corpuses corresponding to the phonetictranscriptions may be obtained from the acoustic model and the acousticlexicon.

However, the current method faces the following problems. Problem 1: incase the phonetic transcriptions of vocabularies used for training theacoustic model is the phonetic transcriptions marked in the dictionary,if nonstandard pronunciation (e.g. unclear retroflex, unclear front andback nasals, etc.) of a user is inputted to the acoustic model,fuzziness of the acoustic model may increase since the nonstandardpronunciation is likely to be mismatched with the phonetictranscriptions marked in the dictionary. For example, in order to copewith the nonstandard pronunciation, the acoustic model may output “ing”that has higher probability for a phonetic spelling “in”, which leads toincrease of an overall error rate. Problem 2: due to differentpronunciation habits in different regions, the nonstandard pronunciationmay vary, which further increases fuzziness of the acoustic model andreduces recognition accuracy. Problem 3: dialects (e.g. standardMandarin, Shanghainese, Cantonese, Minnan, etc.) cannot be recognized.Problem 4: mispronounce words (e.g., “

” in “---

” should be pronounced as “he”, yet many people mispronounce it as “he”)cannot be recognized. Problem 5: because phonetic transcriptions areconverted into vocabularies by the acoustic lexicon, a lot of speechinformation (e.g., accent locations) may lose to influence an accuracyin intension recognition, which leads to increase of an error rate insemanteme recognition.

SUMMARY OF THE INVENTION

The invention is directed to a method for building a language model, aspeech recognition method and an electronic apparatus thereof, capableof eliminating ambiguity produced while mapping the speech to the text,thereby accurately recognizing a language and a semanteme correspondingto speeches of different languages, dialects or different pronunciationhabits.

The invention provides a method for building a language model adapted toan electronic apparatus. The method for building the language modelincludes: receiving a plurality of candidate sentences; and obtaining aplurality of phonetic spellings matching each of words in each of thecandidate sentences and a plurality of word probabilities according to atext corpus, so as to obtain a candidate sentence table corresponding tothe candidate sentences.

The invention provides a speech recognition method adapted to anelectronic apparatus. The speech recognition method includes followingsteps: obtaining a plurality of phonetic transcriptions of the speechsignal according to an acoustic model, and the phonetic transcriptionsincluding a plurality of phones; obtaining a plurality of phoneticspellings matching each of the phonetic transcriptions according to thephonetic transcriptions and a syllable acoustic lexicon; obtaining aplurality of text sequences and a plurality of text sequenceprobabilities from a language model according to the phonetic spellings,and this step includes: matching each of the phonetic spellings with acandidate sentence table, so as to obtain a word probability of each ofthe phonetic spellings corresponding to each of the words in thecandidate sentences; and calculating the word probabilities of thephonetic spellings, so as to obtain the text sequence probabilities,wherein the candidate sentences corresponding to the text sequenceprobabilities are the text sequences; and selecting the text sequencecorresponding to a largest one among the text sequence probabilities asa recognition result of the speech signal.

The invention further provides an electronic apparatus which includes astorage unit and a processing unit. The storage unit stores a pluralityof program code segments. The processing unit is coupled to the storageunit. The processing unit executes a plurality of commands through theprogram code segments. The commands include: receiving a plurality ofcandidate sentences; and obtaining a plurality of phonetic spellingsmatching each of words in each of the candidate sentences and aplurality of word probabilities according to a text corpus, so as toobtain a candidate sentence table corresponding to the candidatesentences.

The invention further provides an electronic apparatus which includes aninput unit, a storage unit and a processing unit. The input unitreceives a speech signal. The storage unit stores a plurality of programcode segments. The processing unit is coupled to the input unit and thestorage unit, and the processing unit executes a plurality of commandsthrough the program code segments. The commands include: obtaining aplurality of phonetic transcriptions of the speech signal according toan acoustic model, and the phonetic transcriptions including a pluralityof phones; obtaining a plurality of phonetic spellings matching thephonetic transcriptions according to the phonetic transcriptions and asyllable acoustic lexicon; obtaining a plurality of text sequences and aplurality of text sequence probabilities from a language model accordingto the phonetic spellings, therein, this commands executed by theprocessing unit include: matching each of the phonetic spellings with acandidate sentence table, so as to obtain a word probability of each ofthe phonetic spellings corresponding to each of the words in thecandidate sentences; and calculating the word probabilities of thephonetic spellings, so as to obtain the text sequence probabilities,wherein the candidate sentences corresponding to the text sequenceprobabilities are the text sequences; and selecting the text sequencecorresponding to a largest one among the text sequence probabilities asa recognition result of the speech signal.

Based on above, when the speech recognition is performed on the speechsignal, the electronic apparatus may obtain the phonetic transcriptionsmatching real pronunciations according to the acoustic model, and obtainthe phonetic spellings matching the phonetic transcriptions from thesyllable acoustic lexicon. Further, according to each of the phoneticspellings, the electronic apparatus may find the text sequence matchingthe phonetic spelling and the text sequence probabilities from thelanguage model. The text sequence corresponding to the largest one amongthe text sequence probabilities is selected as a recognition result ofthe speech signal. Accordingly, the invention is capable of recognizingthe phonetic spelling to the text according to the phonetic spellingcorresponding to the real pronunciations of the speech inputs, therebyeliminating the ambiguity produced while mapping the speech to the text,and retaining the message inputted by the original speech input, so thatthe speech recognition may be more accurate.

To make the above features and advantages of the disclosure morecomprehensible, several embodiments accompanied with drawings aredescribed in detail as follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an electronic apparatus according to anembodiment of the invention.

FIG. 2 is a schematic view of a speech recognition module according toan embodiment of the invention.

FIG. 3 is a flowchart illustrating the speech recognition methodaccording to an embodiment of the invention.

FIG. 4 is a schematic view of a speech recognition module according toan embodiment of the invention.

FIG. 5 is a flowchart illustrating the speech recognition methodaccording to an embodiment of the invention.

FIG. 6 is a flowchart illustrating the speech recognition methodaccording to an embodiment of the invention.

DESCRIPTION OF THE EMBODIMENTS

In traditional method of speech recognition, another common problem isthat a recognition accuracy is easily influenced by a fuzzy sound ofdialects in different regions, pronunciation habits of users, ordifferent languages. Further, a speech recognition of conventional artgenerally outputs in text, thus numerous speech information (e.g., asemanteme that varies based on expression in different tones) may lose.Moreover, during a process for mapping pronunciations to texts, aprobability generally used in conventional art is a probability of onespecific pronunciation to one specific vocabulary. Therefore, homonymsmay be mapped to the vocabularies with the same pronunciation, whichleads to increase of an error rate in the process for mappingpronunciations to texts. Accordingly, the invention proposes a methodfor building a language model, a speech recognition method and anelectronic apparatus thereof, which may improve the recognition accuracyon basis of the original speech recognition. In order to make theinvention more comprehensible, embodiments are described below as theexamples to prove that the invention can actually be realized.

FIG. 1 is a block diagram of an electronic apparatus according to anembodiment of the invention. Referring to FIG. 1, an electronicapparatus 100 includes a processing unit 110, a storage unit 120, and aninput unit 130, also, an output unit 140 may be further included.

The electronic apparatus 100 may be various apparatuses with computingcapabilities, such as a cell phone, a personal digital assistant (PDA) asmart phone, a pocket PC, a tablet PC, a notebook PC, a desktop PC, acar PC, but the invention is not limited thereto.

The processing unit 110 is coupled to the storage unit 120 and the inputunit 130. The processing unit 110 may be a hardware with computingcapabilities (e.g., a chip set, a processor and so on) for executingdata in hardware, firmware and software in the electronic apparatus 100.In the present embodiment, the processing unit 110 is, for example, acentral processing unit (CPU) or other programmable microprocessors, adigital signal processor (DSP), a programmable controller, anapplication specific integrated circuits (ASIC), a programmable logicdevice (PLD) or other similar apparatuses.

The storage unit 120 may store one or more program codes for executingthe speech recognition method as well as data (e.g., a speech signalinputted by a user, an acoustic model, a syllable acoustic lexicon, alanguage model and a text corpus for the speech recognition) and so on.In the present embodiment, the storage unit 120 is, for example, aNon-volatile Memory (NVM), a Dynamic Random Access Memory (DRAM), or aStatic Random Access Memory (SRAM).

The input unit 130 is, for example, a microphone configured to receive avoice from the user, and convert the voice of the user into the speechsignal.

Hereinafter, the speech recognition method of the electronic apparatus100 may be implemented by program codes in the present embodiment. Morespecifically, a plurality of program code segments are stored in thestorage unit 120, and after said program code segments are installed,the processing unit 110 may execute a plurality of commands through theprogram code segments, so as to realize a method of building theacoustic model and the speech recognition method of the presentembodiment. More specifically, the processing unit 110 may build theacoustic model, the syllable acoustic lexicon and the language model byexecuting the commands in the program code segments, and drives a speechrecognition module through the program code segments to execute thespeech recognition method of the present embodiment by utilizing theacoustic model, the syllable acoustic lexicon and the language model.Therein, the speech recognition module may be implemented by computerprogram codes. Or, in another embodiment of the invention, the speechrecognition module may be implemented by a hardware circuit composed ofone or more logic gates. Accordingly, the processing unit 110 of thepresent embodiment may perform the speech recognition on the speechsignal received by the input unit 130 through the speech recognitionmodule, so as to obtain a plurality of string probabilities and aplurality of strings by utilizing the acoustic model, the syllableacoustic lexicon and the language model. Moreover, the processing unit110 may select the string corresponding to a largest one among thestrings probabilities as a recognition result of the speech signal.

In addition, the present embodiment may further include the output unit140 configured to output the recognition result of the speech signal.The output unit 140 is, for example, a display unit such as a CathodeRay Tube (CRT) display, a Liquid Crystal Display (LCD), a PlasmaDisplay, a Touch Display, configured to display a candidate stringcorresponding to the largest one among the string probabilities. Or, theoutput unit 140 may also be a speaker configured to play the candidatestring corresponding to the largest one among the string probabilities.

Ii should be noted that, the processing unit 110 of the presentembodiment may build the acoustic model, the syllable acoustic lexicon,the language model respectively for different languages, dialects orpronunciation habits, and said models and lexicon are stored in thestorage unit 120.

More specifically, the acoustic model is, for example, a statisticalclassifier that adopts a Gaussian Mixture Model to analyze the receivedspeech signals into basic phones, and classify each of the phones tocorresponding basic phonetic transcriptions. Therein, the acoustic modelmay include basic phonetic transcriptions, transition between phones andnon-speech phones (e.g., coughs) for recognizing the speech inputs ofdifferent languages, dialects or pronunciation habits. Generally, thesyllable acoustic lexicon is composed of individual words of thelanguage under recognition, and the individual words are composed ofsounds outputted by the acoustic model through the Hidden Markov Model(HMM). Therein, for the monosyllabic language (e.g., Chinese), thephonetic transcriptions outputted by the acoustic model may be convertedinto corresponding vocabularies through the syllable acoustic lexicon.The language model mainly utilizes a probability statistical method toreveal the inherent statistical regularity of a language unit, whereinN-Gram is widely used for its simplicity and effectiveness.

An embodiment is given for illustration below.

FIG. 2 is a schematic view of a speech recognition module according toan embodiment of the invention. Referring to FIG. 2, a speechrecognition module 200 mainly includes an acoustic model 210, a syllableacoustic lexicon 220, a language model 230 and a decoder 240. Therein,the acoustic model 210 and the syllable acoustic lexicon 220 areobtained by training with a speech database 21, and the language model230 is obtained by training with a text corpus 22. In the presentembodiment, the speech database 21 and the text corpus 22 include aplurality of speech signals being, for example, speech inputs ofdifferent languages, dialects or pronunciation habits.

Referring to FIG. 1 and FIG. 2 together, the acoustic model 210 isconfigured to recognize the speech signals of different languages,dialects or pronunciation habits, so as to recognize a plurality ofphonetic transcriptions matching pronunciations of the speech signal. Inthe present embodiment, the processing unit 110 obtains the acousticmodel 210 through training with the speech signals based on differentlanguages, dialects or pronunciation habits. More specifically, theprocessing unit 110 may receive the speech signals from the speechdatabase 21 and receive the phonetic transcriptions matching thepronunciations in the speech signal, in which the pronunciationcorresponding to each of the phonetic transcriptions includes aplurality of phones. Further, the processing unit 110 may obtain data ofthe phones corresponding to the phonetic transcriptions in the acousticmodel 210 by training according to the speech signals and the phonetictranscriptions. More specifically, the processing unit 110 may obtainthe speech signals corresponding to the speech inputs of differentlanguages, dialects or pronunciation habits from the speech database 21,and obtain feature parameters corresponding to each of the speechsignals by analyzing the phones of the each of the speech signals.Subsequently, a matching relation between the feature parameters of thespeech signal and the phonetic transcriptions may be obtained throughtraining with the feature parameters and the speech signals alreadymarked with the corresponding phonetic transcriptions, so as to buildthe acoustic model 210.

The syllable acoustic lexicon 220 includes a plurality of vocabulariesand fuzzy sound probabilities of each of the phonetic transcriptionsmatching each of the vocabularies. Herein, the processing unit 110 maysearch a plurality of vocabularies matching each of the phonetictranscriptions and the fuzzy sound probabilities of each of thevocabularies matching each of the phonetic transcription through thesyllable acoustic lexicon 220. In the present embodiment, the processingunit 110 obtains the syllable acoustic lexicon through training with thespeech signals based on different languages, dialects or pronunciationhabits. More specifically, the processing unit 110 may obtain the fuzzysound probabilities of each of the vocabularies matching each of thephonetic transcriptions by training with the phonetic transcription ofthe speech signal and the vocabularies respectively corresponding toeach of the speech signals. It should be noted that, the processing unit110 may mark each of the phonetic transcriptions in the speech signalwith a corresponding code. In other words, for each vocabulary with thesame character form but different pronunciations (i.e., the polyphone),such vocabulary includes different phonetic transcriptions forcorresponding to each of the pronunciations. Further, such vocabularyincludes at least one code, and each of the codes is corresponding toone of the different phonetic transcriptions. Accordingly, the syllableacoustic lexicon 220 of the present embodiment may include vocabulariescorresponding to the phonetic transcriptions of the speech inputs havingdifferent pronunciations, and codes corresponding to each of thephonetic transcriptions.

The language model 230 is a design concept based on a history-basedModel, that is, to gather statistics of the relationship between aseries of previous events and an upcoming event according to a rule ofthumb. Herein, the language model 230 is configured to recognize thestring matching the code and the string probabilities of the stringmatching the code according to the codes for different vocabularies. Inthe present embodiment, the processing unit 110 may obtain the languagemodel 230 through training with corpus data based on differentlanguages, dialects or different pronunciation habits. Therein, thecorpus data include a speech input having a plurality of pronunciationsand a string corresponding to the speech input. Herein, the processingunit 110 obtains the string from the text corpus 22, and trains thecodes respectively corresponding to the string and the vocabularies ofthe string, so as to obtain the data of the code matching each string.

The decoder 240 is a core of the speech recognition module 200 dedicatedto search the string outputted with a largest probability possible forthe inputted speech signal according to the acoustic model 210, thesyllable acoustic lexicon 220 and the language model 230. For instance,by utilizing the corresponding phones and syllables obtained from theacoustic model 210 and words or vocabularies obtained from the syllableacoustic lexicon 220, the language model 230 may determine a probabilityfor a series of words becoming a sentence.

The speech recognition method of the invention is described below withreference to said electronic apparatus 100 and said speech recognitionmodule 200. FIG. 3 is a flowchart illustrating the speech recognitionmethod according to an embodiment of the invention. Referring to FIG. 1,FIG. 2 and FIG. 3 together, the speech recognition method of the presentembodiment is adapted to the electronic apparatus 100 for performing thespeech recognition on the speech signal. Therein, the processing unit110 may automatically recognize a language corresponding to the speechsignal for different languages, dialects or pronunciation habits byutilizing the acoustic model 210, the syllable acoustic lexicon 220, thelanguage model 230 and the decoder 240.

In step S310, the input unit 130 receives a speech signal S1, and thespeech signal S1 is, for example, a speech input from a user. Morespecifically, the speech signal S1 is the speech input of a monosyllabiclanguage, and the monosyllabic language is, for example, Chinese.

In step S320, the processing unit 110 may obtain a plurality of phonetictranscriptions of the speech signal S1 according to the acoustic model210, and the phonetic transcriptions includes a plurality of phones.Herein, for the monosyllabic language, the phones are included in eachof the syllables in the speech signal S1, and the syllable iscorresponding to one phonetic transcription. For instance, two simplewords “

” include the syllables being “

” and “

”, and the phones being “

”, “

”, “

”, “´”, “

”, “

” and “{grave over ( )}”. Therein, “

”, “

”, “´” correspond to the phonetic transcription “qián”, and “

”, “

”, “{grave over ( )}” correspond to the phonetic transcription “jìn”.

In the present embodiment, the processing unit 110 may select a trainingdata from the acoustic model 210 according to a predetermined setting,and the training data is one of training results of different languages,dialects or different pronunciation habits. Herein, the processing unit110 may search the phonetic transcriptions matching the speech signal S1by utilizing the acoustic model 210 and selecting the speech signal inthe training data and the basic phonetic transcriptions corresponding tothe speech signal.

More specifically, the predetermined setting refers to which languagethe electronic apparatus 100 is set to perform the speech recognitionwith. For instance, it is assumed that the electronic apparatus 100 isset to perform the speech recognition according to the pronunciationhabit of a northern, such that the processing unit 110 may select thetraining data trained based on the pronunciation habit of the northernfrom the acoustic model 210. Similarly, in case the electronic apparatus100 is set to perform the speech recognition of Minnan, the processingunit 110 may select the training data trained based on Minnan from theacoustic model 210. The predetermined settings listed above are merelyexamples. In other embodiments, the electronic apparatus 100 may also beset to perform the speech recognition according to other languages,dialects or pronunciation habits.

Furthermore, the processing unit 110 may calculate the phonetictranscription matching probabilities of the phones in the speech signalS1 matching each of the basic phonetic transcriptions according to theselected acoustic model 210 and the phones in the speech signal S1.Thereafter, the processing unit 110 may select each of the basicphonetic transcriptions corresponding to a largest one among thephonetic transcription matching probabilities being calculated to beused as the phonetic transcriptions of the speech signal S1. Morespecifically, the processing unit 110 may divide the speech signal S1into a plurality of frames, among which any two adjacent frames may havean overlapping region. Thereafter, a feature parameter is extracted fromeach frame to obtain one feature vector. For example, Mel-frequencyCepstral Coefficients (MFCC) may be used to extract 36 featureparameters from the frames to obtain a 36-dimensional feature vector.Herein, the processing unit 110 may match the feature parameter of thespeech signal S1 with the data of the phones provided by the acousticmodel 210, so as to calculate the phonetic transcription matchingprobabilities of each of the phones in the speech signal S1 matchingeach of the basic phonetic transcriptions. Accordingly, the processingunit 110 may select each of the basic phonetic transcriptionscorresponding to the largest one among the phonetic transcriptionmatching probabilities to be used as the phonetic transcriptions of thespeech signal S1.

In step S330, the processing unit 110 may obtain a plurality ofvocabularies matching the phonetic transcriptions according to each ofthe phonetic transcriptions and the syllable acoustic lexicon 220.Therein, the syllable acoustic lexicon 220 includes the vocabulariescorresponding to the phonetic transcriptions, and each of thevocabularies includes at least one code. Further, for each vocabularywith the same character form but different pronunciations (i.e., thepolyphone), each code of such vocabulary includes is corresponding toone phonetic transcription in the vocabulary.

Herein, the processing unit 110 may also select a training data from thesyllable acoustic lexicon 220 according to a predetermined setting, andthe training data is one of training results of different languages,dialects or different pronunciation habits. Further, the processing unit110 may obtain the fuzzy sound probabilities of the phonetictranscriptions matching each of the vocabularies according to thetraining data selected from the syllable acoustic lexicon 220 and eachof the phonetic spellings of the speech signal S1. It should be notedthat, the polyphone may have different phonetic transcriptions based ondifferent languages, dialects or pronunciation habits. Therefore, in thesyllable acoustic lexicon 220, the vocabulary corresponding to each ofthe phonetic transcriptions includes the fuzzy sound probabilities, andthe fuzzy sound probabilities may be changed according differentlanguages, dialects or pronunciation habits. In other words, by usingthe training data trained based on different languages, dialects orpronunciation habits, the different fuzzy sound probabilities areprovided for each of the phonetic transcriptions and the correspondingvocabularies in the syllable acoustic lexicon 220.

For instance, when the training data trained based on the pronunciationof the northern the syllable acoustic lexicon 220 is selected as thepredetermined setting, for the phonetic transcription “fú”, thecorresponding vocabulary includes higher fuzzy sound probabilities forbeing “

”, “

”, “

” and the corresponding vocabulary of “fú” includes lower fuzzy soundprobabilities for being “

”, “

”, “

”. As another example, when the training data trained based on thepronunciation habits of most people in the syllable acoustic lexicon 220is selected as the predetermined setting, for the phonetic transcription“hè”, the corresponding vocabulary includes higher fuzzy soundprobabilities for being “

”, “

”, “

”. It should be note that, most people tended to pronounce thevocabulary “

” in “

” as “

{grave over ( )}” (“hè”). Therefore, the fuzzy sound probability of “he”corresponding to “

” is relatively higher. Accordingly, by selecting the vocabularycorresponding to the largest one among the fuzzy sound probabilities,the processing unit 110 may obtain the vocabulary matching each of thephonetic transcriptions in the speech signal S1 according to specificlanguages, dialects or pronunciation habits.

On the other hand, the polyphone having different pronunciations mayhave different meanings based on the different pronunciations. Thus, inthe present embodiment, for the polyphone with the same character formbut different pronunciations, the processing unit 110 may obtain thecode of each of the vocabularies, so as to differentiate thepronunciations of each of the vocabularies. Take the vocabulary “

” as the polyphone for example, the phonetic transcriptions thereof forthe pronunciation in Chinese may be, for example, “cháng” or “zh{hacekover (a)}ng”, and the phonetic transcriptions of “

” may even be, for example, “cêng”, “zêng” (Cantonese tone) in terms ofdifferent dialects or pronunciation habits. Therefore, for the phonetictranscriptions of “

”, the syllable acoustic lexicon may have said phonetic transcriptionscorresponding to four codes, such as “c502”, “c504”, “c506” and “c508”.Herein, above-said codes are merely examples, which may be representedin other formats (e.g., one of value, alphabet or symbol or acombination thereof). In other words, the syllable acoustic lexicon 220of the present embodiment may regard the polyphone as differentvocabularies, so that the polyphone may correspond to the strings havingdifferent meanings in the language model 230. Accordingly, when theprocessing unit 110 obtains the polyphone having different phonetictranscriptions by utilizing the syllable acoustic lexicon 220, since thedifferent phonetic transcriptions of the polyphone may correspond todifferent codes, the processing unit 110 may differentiate the differentpronunciations of the polyphone, thereby retaining a diversity of thepolyphone in different pronunciations.

In step S340, the processing unit 110 may obtain a plurality of stringsand a plurality of string probabilities from the language model 230according to the codes of each of the vocabularies. More specifically,the language model 230 is configured to recognize the string matchingthe code and the string probabilities of the code matching the stringaccording to the codes for different vocabularies. Accordingly, theprocessing unit 110 may calculate the string probabilities of the codematching each of the strings through the language model 230 according tothe codes of the vocabularies obtained from the syllable acousticlexicon 220. Therein, if the string probability calculated by theprocessing unit 110 is relatively lower, it indicates that a probabilityfor the phonetic transcription corresponding to code to be used by thestring is lower. Otherwise, if the string probability calculated by theprocessing unit 110 is relatively higher, it indicates that aprobability for the phonetic transcription corresponding to code to beused by the string is higher.

Referring back to the polyphone “

”, the code corresponding to the phonetic transcription thereof (e.g.,“cháng”, “zh{hacek over (a)}ng”, “cêng” and “zêng”) may be, for example,“c502”, “c504”, “c506” and “c508”. Hereinafter, it is assumed that nameof “

” (i.e., mayor) of “

” (i.e., Nanjing) is “

”. If the string probability for the code “c504” corresponding to thephonetic transcription “zh{hacek over (a)}ng” of “

” in the string “ . . .

{hacek over ( )})‘

” is quite high, the processing unit 110 may determine that aprobability for the vocabulary “

” with the phonetic transcription “zh{hacek over (a)}ng” to appear in “

” is higher, and a probability for the vocabulary “

” to come before “

” is also higher. Further, at the same time, the processing unit 110 maydetermine that the string probability for the code “c504” correspondingto the phonetic transcription “zh{hacek over (a)}ng” of “

” in the string“

(

’)‘

” is relatively lower.

From another prospective, if the string probability for the code “c502”corresponding to the phonetic transcription “cháng” of “

” in the string “

(

’)

” is relatively higher, the processing unit 110 may determine that aprobability for the vocabulary “

” with the phonetic transcription “cháng” to appear in “

” is higher, and a probability for the vocabulary “

” to come before “

” is also higher. In this case, the processing unit 110 may determinethat string probability for the code “c502” corresponding to thephonetic transcription “cháng” of the vocabulary “

” in the string “

(

)’

” is relatively lower.

As another example, for the vocabulary “

”, the phonetic transcription thereof may be “cháng” or “zh{hacek over(a)}ng”. Despite that when the vocabulary “

” comes before the vocabulary “

”, “

” is usually pronounced with the phonetic transcription “zh{hacek over(a)}ng”, but it is also possible to pronounce it with the phonetictranscription “cháng”. For instance, “

” may refer to “‘

(

)’

’” (i.e., Nanjing city-Yangtze river bridge)”, or may also refer to “‘

(

)’-‘

’” (Nanjing-mayor Jiāng dà qiáo). Therefore, based on the code “c502”corresponding to the phonetic transcription “cháng” and the code “c504”corresponding to the phonetic transcription “zh{hacek over (a)}ng”, theprocessing unit 110 may calculate the string probabilities for the codes“c502” and “c504” in the string “

” according to the language model 230.

For instance, if the string probability for the code “c502”corresponding to the phonetic transcription “cháng” in the string “

” is relatively higher, it indicates that a probability for thevocabulary “

” with the phonetic transcription “cháng” in the string “‘

(

)

” is also higher. Or if the string probability for the code “c504”corresponding to the phonetic transcription “zh{hacek over (a)}ng” inthe string “

” is relatively higher, it indicates that a probability for thevocabulary “

” with the phonetic transcription “zh{hacek over (a)}ng” in the string “

(

{hacek over ( )})’-‘

’” is also higher.

Thereafter, in step S350, the processing unit 110 may select the stringcorresponding to a largest one among the string probabilities to be usedas a recognition result S2 of the speech signal S1. For instance, theprocessing unit 110 calculates, for example, a product of the fuzzysound probabilities from the syllable acoustic lexicon 220 and thestring probabilities from the language model 230 as associatedprobabilities, and selects a largest one among the associatedprobabilities of the fuzzy sound probabilities and the stringprobabilities to be used as the recognition result S2 of the speechsignal S1. In other words, the processing unit 110 is not limited toonly select the vocabulary best matching the phonetic transcription fromthe syllable acoustic lexicon 220, rather, the processing unit 110 mayalso select the string corresponding to the largest one among the stringprobabilities in the language model 230 as the recognition result S2according to the vocabularies matching the phonetic transcription andthe corresponding codes obtained from the syllable acoustic lexicon 220.Of course, the processing unit 110 of the present embodiment may alsoselect the vocabulary corresponding to the largest one among the fuzzysound probabilities in the syllable acoustic lexicon 220 to be used as amatched vocabulary of each phonetic transcription of the speech signal;calculate the string probabilities obtained in the language model 230for each of the codes according to the matched vocabulary; and calculatethe product of the fuzzy sound probabilities and the stringprobabilities as the associated probabilities, thereby selecting thestring corresponding to the largest one among the associatedprobabilities.

More specifically, referring still to the polyphone “

” and the vocabulary “

”, the phonetic transcriptions of the “

” may be, for example, “cháng”, “zh{hacek over (a)}ng”, “cêng” and“zêng” which are respectively corresponding to the codes “c502”, “c504”,“c506” and “c508”, respectively. Herein, when the phonetic transcription“cháng” has the fuzzy sound probability of the vocabulary “

” obtained through the syllable acoustic lexicon 220 being relativelyhigher, the processing unit 110 may select the string corresponding tothe largest one among the string probabilities in the language model 230as the recognition result according to the code “c502” corresponding to“

” and the phonetic transcription “cháng”. For instance, if the code“c502” of “

” in the string “

(

)’

” has the largest one among the string probabilities, the processingunit 110 may obtain the string “

” as the recognition result. However, if the code “c502” of “

” in the string “

(

(

)

’” has the largest one among the string probabilities, the processingunit 110 may obtain the string “‘

(

)

” as the recognition result. Or, when the phonetic transcription“zh{hacek over (a)}ng” has the fuzzy sound probability of the vocabulary“

” obtained through the syllable acoustic lexicon 220 being relativelyhigher, the processing unit 110 may select string corresponding to thelargest one among the string probabilities in the language model 230 asthe recognition result according to the code “c504” corresponding to “

” and the phonetic transcription “zh{hacek over (a)}ng”. For instance,if the code “c504” of “

” in the string “‘

’” has the largest one among the string probabilities, the processingunit 110 may obtain the string “‘

’” as the recognition result. Accordingly, besides that the phonetictranscription and the vocabulary corresponding to the phonetictranscription may be outputted, the electronic apparatus 100 may alsoobtain the fuzzy sound probabilities of the phonetic transcriptionmatching the vocabulary under different languages, dialects orpronunciation habits. Further, according to the codes of the vocabulary,the electronic apparatus 100 may obtain the string probabilities of thevocabulary applied in different strings, so that the string matching thespeech signal S1 may be recognized more accurately to improve theaccuracy of the speech recognition.

Based on above, in the method of building the acoustic model, the speechrecognition method and the electronic apparatus of the presentembodiment, the electronic apparatus may build the acoustic model, thesyllable acoustic lexicon and the language model by training the speechsignal based on different languages, dialects or different pronunciationhabits. Further, for the polyphone having more than one pronunciation,the electronic apparatus may give different codes for each of phonetictranscriptions of the polyphone, thereby retaining a diversity of thepolyphone in different pronunciations. Therefore, when the speechrecognition is performed on the speech signal, the electronic apparatusmay obtain the vocabulary matching real pronunciations from the syllableacoustic lexicon according to the phonetic transcriptions obtained fromthe acoustic model. In particular, since the syllable acoustic lexiconincludes the vocabulary having one or more phonetic transcriptions forcorresponding to the code of each of the phonetic transcriptions, thusthe electronic apparatus may obtain the matched string and the stringprobabilities thereof according to each of the codes. Accordingly, theelectronic apparatus may select the string corresponding to the largestone among the string probabilities as the recognition result of thespeech signal.

As a result, the invention may perform decoding in the acoustic model,the syllable acoustic lexicon, and the language model according to thespeech inputs of different languages, dialects or differentpronunciation habits. Further, besides that a decoding result may beoutputted according to the phonetic transcription and the vocabularycorresponding to the phonetic transcription, the fuzzy soundprobabilities of the phonetic transcription matching the vocabularyunder different languages, dialects or pronunciation habits as well asthe string probabilities of the vocabulary applied in different stringsmay also be obtained. Accordingly, the largest one among saidprobabilities may be outputted as the recognition result of the speechsignal. In comparison with traditional methods, the invention is capableof accurately converting sound to text as well knowing the types of thelanguages, dialects or pronunciation habits. This may facilitate insubsequent machine speech conversations, such as direct answer inCantonese for inputs pronounced in Cantonese. In addition, the inventionmay also differentiate meanings of pronunciations of the polyphone, sothat the recognition result of the speech signal may be more close tothe meaning corresponding to the speech signal.

It should be noted that, in order to avoid loss of speech messages(e.g., a semanteme that varies based on expression in different tones)during the process for mapping pronunciations to texts, outputs of thephonetic transcription sequence and the syllable sequence correspondingto the phonetic transcription sequence may be sequentially obtained fromthe decoding result obtained according to the speech recognition methodof the invention. Meanwhile, probabilities of the phonetic transcriptionsequence matching the syllable sequence under different languages,dialects or pronunciation habits, and probabilities of the syllablesequence being applied in different text sequence may also be obtained.Accordingly, the largest one among said probabilities may be outputtedas the recognition result of the speech signal. As a result, theinvention may further improve the accuracy of the speech recognition onbasis of the original speech recognition. In order to make the inventionmore comprehensible, embodiments are described below as the examples toprove that the invention can actually be realized.

The present embodiment is also described by using the electronicapparatus 100 depicted in FIG. 1. The speech recognition method of theelectronic apparatus 100 may also be implemented by program codes in thepresent embodiment. More specifically, a plurality of program codesegments may be stored in the storage unit 120, and after said programcode segments are installed, the processing unit 110 may execute aplurality of commands through the program code segments, so as torealize the speech recognition method of the present embodiment. Morespecifically, the processing unit 110 may build a speech recognitionmodule including the acoustic model, the syllable acoustic lexicon andthe language model by executing a plurality of commands in the programcode segments. Further, the processing unit 110 may drive the speechrecognition module through the program code segments to execute thespeech recognition method of the present embodiment by utilizing theacoustic model, the syllable acoustic lexicon and the language model.Accordingly, the processing unit 110 of the present embodiment mayperform the speech recognition on the speech signal received by theinput unit 130 through the speech recognition module, so as to obtain aplurality of syllable sequence probabilities and a plurality of syllablesequences by utilizing the acoustic model, the syllable acoustic lexiconand the language model. Moreover, the processing unit 110 may select thesyllable sequence or text sequence corresponding to a largest one amongthe phonetic spelling sequence probabilities as a recognition result ofthe speech signal.

Of course, the present embodiment may further include the output unit140 configured to output the recognition result of the speech signal.For instance, the phonetic spelling sequence corresponding to thelargest one among the phonetic spelling sequence probabilities or thestrings corresponding to the phonetic spelling sequence may be displayedby the display unit 140. Or, the output unit 140 may also be a speakerconfigured to play the phonetic spelling sequence by voice. Further,details regarding the electronic apparatus 100 suitable for the speechrecognition method of the present embodiment may refer to the same inthe foregoing embodiment, thus related description thereof is omittedhereinafter.

An embodiment is further provided below and served to illustrate thespeech recognition method of the present embodiment with reference tothe electronic apparatus 100 depicted in FIG. 1.

FIG. 4 is a schematic view of a speech recognition module according toan embodiment of the invention. Referring to FIG. 1 and FIG. 4, a speechrecognition module 400 mainly includes an acoustic model 410, a syllableacoustic lexicon 420, a language model 430 and a decoder 440. Theacoustic model 410 and the syllable acoustic lexicon are obtained bytraining with a speech database 41, and the language model 430 isobtained by training with a text corpus 42. Therein, the speech database41 and the text corpus 42 include a plurality of speech signals being,for example, speech inputs of different languages, dialects orpronunciation habits, and the text corpus 42 further includes phoneticspellings corresponding to the speech signals. In the presentembodiment, the processing unit 110 may build the acoustic model 410,the syllable acoustic lexicon 420, the language model 430 respectivelythrough training with the speech recognition for different languages,dialects or pronunciation habits, and said models and lexicon are storedin the storage unit 120 to be used in the speech recognition method ofthe present embodiment.

More specifically, the acoustic model 410 is configured to recognize thespeech signals of different languages, dialects or pronunciation habits,so as to recognize a plurality of phonetic transcriptions matchingpronunciations of the speech signal. Furthermore, the acoustic model 410is, for example, e, a statistical classifier that adopts a GaussianMixture Model to analyze the received speech signals into basic phones,and classify each of the phones to corresponding basic phonetictranscriptions. Therein, the acoustic model 410 may include thecorresponding basic phonetic transcriptions, transition between phonesand non-speech phones (e.g., coughs) for recognizing the speech inputsof different languages, dialects or pronunciation habits. In the presentembodiment, the processing unit 110 obtains the acoustic model 410through training with the speech signals based on different languages,dialects or pronunciation habits. More specifically, the processing unit110 may receive the speech signals from the speech database 41 andreceive the phonetic transcriptions matching the pronunciations in thespeech signal, in which the pronunciation corresponding to each of thephonetic transcriptions includes a plurality of phones. Further, theprocessing unit 110 may obtain data of the phones corresponding to thephonetic transcriptions in the acoustic model 410 by training accordingto the speech signals and the phonetic transcriptions. Morespecifically, the processing unit 110 may obtain the speech signalscorresponding to the speech inputs of different languages, dialects orpronunciation habits from the speech database 41, and obtain featureparameters corresponding to each of the speech signals by analyzing thephones of the each of the speech signals. Subsequently, a matchingrelation between the feature parameters of the speech signal and thephonetic transcriptions may be obtained through training with thefeature parameters and the speech signals already marked with thecorresponding phonetic transcriptions, so as to build the acoustic model410.

The processing unit 110 may map the phonetic transcriptions outputted bythe acoustic model 410 to the corresponding syllables through thesyllable acoustic lexicon 420. Therein, the syllable acoustic lexicon420 includes a plurality of phonetic transcription sequences and thesyllable mapped to each of the phonetic transcription sequences. Itshould be noted that, each of the syllables includes a tone, and thetone refers to Yin, Yang, Shang, Qu, and Neutral tones. In terms ofdialects, the phonetic transcription may also include other tones. Inorder to retain the pronunciations and tones outputted by the user, theprocessing unit 110 may map the phonetic transcriptions to thecorresponding syllables with the tones according to the phonetictranscriptions outputted by the acoustic model 410.

More specifically, the processing unit 110 may map the phonetictranscriptions to the syllables through the syllable acoustic lexicon420. Furthermore, according to the phonetic transcriptions outputted bythe acoustic model 210, the processing unit 110 may output the syllablehaving the tones from the syllable acoustic lexicon 420, calculate aplurality of syllable sequence probabilities matching the phonetictranscriptions outputted by the acoustic model 410, and select thesyllable sequence corresponding to a largest one among the syllablesequence probabilities to be used as the phonetic spellingscorresponding to the phonetic transcriptions. For instance, it isassumed that the phonetic transcriptions outputted by the acoustic model410 are “b” and “a”, the processing unit 110 may obtain the phoneticspelling having the tone being “ba” (Shang tone) through the syllableacoustic lexicon 420.

According to the phonetic spellings for different vocabularies and anintonation information corresponding to the phonetic spellings, thelanguage model 430 is configured to recognize the phonetic spellingsequence matching the phonetic spelling, and obtain the phoneticspelling sequence probabilities of the phonetic spelling matching thephonetic spelling sequence. The phonetic spelling sequence is, forexample, the phonetic spellings for indicating the related vocabulary.More specifically, the language model 430 is a design concept based on ahistory-based Model, that is, to gather statistics of the relationshipbetween a series of previous events and an upcoming event according to arule of thumb. The language model 430 may utilize a probabilitystatistical method to reveal the inherent statistical regularity of alanguage unit, wherein N-Gram is widely used for its simplicity andeffectiveness. In the present embodiment, the processing unit 110 mayobtain the acoustic model 430 through training with corpus data based ondifferent languages, dialects or different pronunciation habits.Therein, the corpus data include a speech input having a plurality ofpronunciations and a phonetic spelling sequence corresponding to thespeech input. Herein, the processing unit 110 may obtain the phoneticspelling sequence from the text corpus 42, and obtains data (e.g., thephonetic spelling sequence probabilities for each of the phoneticspelling and the intonation information matching the phonetic spellingsequence) of the phonetic spellings having different tones matching eachof phonetic spelling sequences by training the phonetic spellingsequence with the corresponding tones.

The decoder 440 is a core of the speech recognition module 400 dedicatedto search the phonetic spelling sequence outputted with a largestprobability possible for the inputted speech signal according to theacoustic model 410, the syllable acoustic lexicon 420 and the languagemodel 430. For instance, by utilizing the corresponding phonetictranscription obtained from the acoustic model 410 and the correspondingphonetic spelling obtained from the syllable acoustic lexicon 420, thelanguage model 430 may determine probabilities for a series of phoneticspelling sequences becoming a semanteme that the speech signal intendedto express.

The speech recognition method of the invention is described below withreference to said electronic apparatus 100 depicted in FIG. 1 and saidspeech recognition module 400. FIG. 5 is a flowchart illustrating thespeech recognition method according to an embodiment of the invention.Referring to FIG. 1, FIG. 4 and FIG. 5 together, the speech recognitionmethod of the present embodiment is adapted to the electronic apparatus100 for performing the speech recognition on the speech signal. Therein,the processing unit 110 may automatically recognize a semantemecorresponding to the speech signal for different languages, dialects orpronunciation habits by utilizing the acoustic model 410, the syllableacoustic lexicon 420, the language model 430 and the decoder 440.

In step S510, the input unit 130 receives a speech signal S1, and thespeech signal S1 is, for example, a speech input from a user. Morespecifically, the speech signal S1 is the speech input of a monosyllabiclanguage, and the monosyllabic language is, for example, Chinese.

In step S520, the processing unit 110 may obtain a plurality of phonetictranscriptions of the speech signal S1 according to the acoustic model410, and the phonetic transcriptions includes a plurality of phones.Herein, for the monosyllabic language, the phones are included in thespeech signal S1, and the so-called phonetic transcription refers to asymbol that represents the pronunciation of the phone, namely, each ofthe phonetic transcription represents one phone. For instance, Chinesecharacter “

” may have different pronunciations based on different language ordialects. For example, in standard Mandarin, the phonetic transcriptionof “

” is “fú”, whereas in Chaoshan, the phonetic transcription of “

” is “hog4”. As another example, the phonetic transcription of is “

” is “rén” in standard Mandarin. In Cantonese, the phonetictranscription of “

” is “jan4”. In Minnan, the phonetic transcription of “

” is “lang2”. In Guangyun, the phonetic transcription of “

” is “nin”. In other words, each of the phonetic transcriptions obtainedby the processing unit 110 from the acoustic model 410 is directlymapped to the pronunciation of the speech signal S1.

In order to increase an accuracy for mapping the pronunciation of thespeech signal S1 to the phonetic transcription, the processing unit 110of the present embodiment may select a training data from the acousticmodel 410 according to a predetermined setting, and the training data isone of training results of different languages, dialects or differentpronunciation habits. Accordingly, the processing unit 110 may searchthe phonetic transcriptions matching the speech signal S1 by utilizingthe acoustic model 410 and selecting the speech signals in the trainingdata and the basic phonetic transcriptions corresponding to the speechsignals.

More specifically, the predetermined setting refers to which languagethe electronic apparatus 100 is set to perform the speech recognitionwith. For instance, it is assumed that the electronic apparatus 100 isset to perform the speech recognition according to the pronunciationhabit of a northern, such that the processing unit 110 may select thetraining data trained based on the pronunciation habit of the northernfrom the acoustic model 410. Similarly, in case the electronic apparatus100 is set to perform the speech recognition of Minnan, the processingunit 110 may select the training data trained based on Minnan from theacoustic model 410. The predetermined settings listed above are merelyexamples. In other embodiments, the electronic apparatus 100 may also beset to perform the speech recognition according to other languages,dialects or pronunciation habits.

Furthermore, the processing unit 110 may calculate the phonetictranscription matching probabilities of the phones in the speech signalS1 matching each of the basic phonetic transcriptions according to theselected acoustic model 210 and the phones in the speech signal S1.Thereafter, the processing unit 110 may select each of the basicphonetic transcriptions corresponding to a largest one among thephonetic transcription matching probabilities being calculated to beused as the phonetic transcriptions of the speech signal S1. Morespecifically, the processing unit 110 may divide the speech signal S1into a plurality of frames, among which any two adjacent frames may havean overlapping region. Thereafter, a feature parameter is extracted fromeach frame to obtain one feature vector. For example, Mel-frequencyCepstral Coefficients (MFCC) may be used to extract 36 featureparameters from the frames to obtain a 36-dimensional feature vector.Herein, the processing unit 110 may match the feature parameter of thespeech signal S1 with the data of the phones provided by the acousticmodel 410, so as to calculate the phonetic transcription matchingprobabilities of each of the phones in the speech signal S1 matchingeach of the basic phonetic transcriptions. Accordingly, the processingunit 110 may select each of the basic phonetic transcriptionscorresponding to the largest one among the phonetic transcriptionmatching probabilities to be used as the phonetic transcriptions of thespeech signal S1.

In step S530, the processing unit 110 may obtain a plurality of phoneticspellings matching the phonetic transcriptions and the intonationinformation corresponding to each of the phonetic spellings according toeach of the phonetic transcriptions and the syllable acoustic lexicon420. Therein, the syllable acoustic lexicon 420 includes a plurality ofphonetic spellings matching each of the phonetic transcriptions, andpossible tones for the pronunciations of such phonetic transcriptions indifferent semantemes when the phonetic transcription is pronounced. Inthe present embodiment, the processing unit 110 may also select atraining data from the syllable acoustic lexicon 420 according to apredetermined setting, and the training data is one of training resultsof different languages, dialects or different pronunciation habits.Further, the processing unit 110 may obtain phonetic spelling matchingprobabilities of the phonetic transcription matching each of thephonetic spellings according to the training data selected from thesyllable acoustic lexicon 420 and each of the phonetic transcriptions ofthe speech signal S1. It should be noted that, each of the vocabulariesmay have different phonetic transcriptions based on different languages,dialects or pronunciation habits, and each of the vocabularies may alsoinclude pronunciations having different tones based on differentsemantemes. Therefore, in the syllable acoustic lexicon 420, thephonetic spelling corresponding to each of the phonetic transcriptionsincludes the phonetic spelling matching probabilities, and the phoneticspelling matching probabilities may vary based on different languages,dialects or pronunciation habits. In other words, by using the trainingdata trained based on different languages, dialects or differentpronunciation habits, different phonetic spelling matching probabilitiesare provided to each of the phonetic transcriptions and thecorresponding phonetic spelling in the syllable acoustic lexicon 420.

For instance, when the syllable acoustic lexicon 420 with the trainingdata trained based on the pronunciation of the northern is selected asthe predetermined setting, for the phonetic transcription pronounced as“fú”, the phonetic spelling thereof include a higher phonetic spellingmatching probability for being “Fit” and a lower phonetic spellingmatching probability for being “Hú”. More specifically, in case thevocabulary “

” is said by the northern, the processing unit 110 may obtain thephonetic transcription “fú” from the acoustic model 410, and obtain thephonetic spelling “Fú” as the higher phonetic spelling matchingprobability and the phonetic spelling “Hú” as the lower phoneticspelling matching probability from the syllable acoustic lexicon 420.Herein, the phonetic spelling corresponding to the phonetictranscription “fú” may have different phonetic spelling matchingprobabilities based on different pronunciation habits in differentregions.

As another example, when the syllable acoustic lexicon 420 with thetraining data trained based on the pronunciation of most people isselected as the predetermined setting, for the phonetic transcriptionpronounced as “yíng”, the phonetic spelling thereof include a higherphonetic spelling matching probability for being “Yíng” and a lowerphonetic spelling matching probability for being “Xi{hacek over (a)}ng”.More specifically, when the vocabulary “

” is said by the user, the processing unit 110 may obtain the phonetictranscription “yíng” from the acoustic model 410, and obtain phoneticspelling matching probabilities corresponding to the phonetic spellings“Xi{hacek over (a)}ng” and “Yíng” in the syllable acoustic lexicon 420,respectively. Herein, the phonetic spelling corresponding to thephonetic transcription “yíng” may have different phonetic spellingmatching probabilities based on different semantemes.

It should be noted that, the speech input composed of the same text maybecome the speech signals having different tones based on differentsemantemes or intentions. Therefore, the processing unit 110 may obtainthe phonetic spelling matching the tones according to the phoneticspelling and the intonation information in the syllable acoustic lexicon420, thereby differentiating the phonetic spellings of differentsemantemes. For instance, for the speech input corresponding to asentence “

”, a semanteme thereof may be of interrogative or affirmative sentences.Namely, the tone corresponding to the vocabulary “

” in “

?” is relatively higher, and the tone corresponding to the vocabulary “

” in “

” is relatively lower. More specifically, for the phonetic transcriptionpronounced as “h{hacek over (a)}o”, the processing unit 110 may obtainthe phonetic spelling matching probabilities corresponding to thephonetic spellings “háo” and “h{hacek over (a)}o” from the syllableacoustic lexicon 420.

In other words, the processing unit 110 may recognize the speech inputshaving the same phonetic spelling but different tones according to thetones in the syllable acoustic lexicon 420, so that the phoneticspellings having different tones may correspond to the phonetic spellingsequences having different meanings in the language model 430.Accordingly, when the processing unit 110 obtains the phonetic spellingsby utilizing the syllable acoustic lexicon 420, the intonationinformation of the phonetic spelling may also be obtained at the sametimes, thus the processing unit 110 is capable of recognizing the speechinputs having different semantemes.

In step S540, the processing unit 110 may obtain a plurality of phoneticspelling sequences and a plurality of phonetic spelling sequenceprobabilities from the language model 430 according to each of thephonetic spelling and the intonation information. Herein, differentintonation information in the language model 430 may be divided intodifferent semantemes, and the semantemes are corresponding to differentphonetic spelling sequences. Accordingly, the processing unit 110 maycalculate the phonetic spelling sequence probability for the phoneticspelling and the intonation information matching each of the phoneticspelling sequences through the language model 430 according to thephonetic spelling and the intonation information obtained from thesyllable acoustic lexicon 420, thereby finding the phonetic spellingsequence matching the intonation information.

More specifically, the language model 430 of the present embodimentfurther includes a plurality of phonetic spelling sequence correspondingto a plurality of keywords, and the keywords are, for example,substantives such as place names, person names or other fixed terms orphrases. For example, the language model 430 includes the phoneticspelling sequence “Cháng-Jiāng-Dà-Qiáo” corresponding to the keyword “

”. Therefore, when the processing unit 110 matches the phonetic spellingand the intonation information obtained from the syllable acousticlexicon 420 with the phonetic spelling sequence in the language model430, whether the phonetic spelling matches the phonetic spellingsequence corresponding to each of the keywords in the language model 430may be compared. In case the phonetic spelling matches the phoneticspelling sequence corresponding to the keyword, the processing unit 110may obtain higher phonetic spelling sequence probabilities. Accordingly,if the phonetic spelling sequence probability calculated by theprocessing unit 110 is relatively lower, it indicates that a probabilityfor the intonation information corresponding to phonetic spelling to beused by the phonetic spelling sequence is lower. Otherwise, if thephonetic spelling sequence probability calculated by the processing unit110 is relatively higher, it indicates that a probability for theintonation information corresponding to phonetic spelling to be used bythe phonetic spelling sequence is higher.

Thereafter, in step S550, the processing unit 110 may select thephonetic spelling sequence corresponding to a largest one among thephonetic spelling sequence probabilities to be used as a recognitionresult S2 of the speech signal S1. For instance, the processing unit 110calculates, for example, a product of the phonetic spelling matchingprobabilities from the syllable acoustic lexicon 420 and the phoneticspelling sequence probabilities from the language model 430 asassociated probabilities, and selects a largest one among the associatedprobabilities of the phonetic spelling matching probabilities and thephonetic spelling sequence probabilities to be used as the recognitionresult S2 of the speech signal S1. In other words, the processing unit110 is not limited to only select the phonetic spelling and theintonation information best matching the phonetic transcription from thesyllable acoustic lexicon 420, the processing unit 110 may also selectthe phonetic spelling sequence corresponding to the largest one amongthe phonetic spelling sequence probabilities in the language model 430to be used as the recognition result S2 according to the phoneticspellings and the intonation information matching the phonetictranscriptions obtained from the syllable acoustic lexicon 420. Ofcourse, the processing unit 110 of the present embodiment may alsoselect the phonetic spelling and the intonation informationcorresponding to the largest one among the phonetic spelling matchingprobabilities in the syllable acoustic lexicon 420 to be used as amatched phonetic spelling of each phonetic transcription of the speechsignal; calculate the phonetic spelling sequence probabilities obtainedin the language model 430 for each of the phonetic spellings accordingto the matched phonetic spelling; and calculate the product of thephonetic spelling matching probabilities and the phonetic spellingsequence probabilities as the associated probabilities, therebyselecting the phonetic spelling corresponding to the largest one amongthe associated probabilities.

It should be noted that, the phonetic spelling sequence obtained by theprocessing unit 110 may also be converted into corresponding textsequence through a semanteme recognition module (not illustrated), andthe semanteme recognition module may search a text corresponding to thephonetic spelling sequence according to a phonetic spelling-basedrecognition database (not illustrated). More specifically, therecognition database includes data of the phonetic spelling sequencecorresponding to the text sequence, such that the processing unit 110may further convert the phonetic spelling sequence into the textsequence through the semanteme recognition module and the recognitiondatabase, and the text sequence may then be displayed by the output unit140 for the user.

An embodiment is further provided below and served to illustrate thespeech recognition method of the present embodiment, in which it isassumed that the speech signal S1 from the user is corresponding to aninterrogative sentence “

”. Herein, the input unit 130 receives the speech signal S1, and theprocessing unit 110 obtains a plurality of phonetic transcriptions(i.e., “nán”, “jīng”, “shì”, “cháng”, “jiāng”, “dà”, “qiáo”) of thespeech signal S1 according the acoustic model 410. Next, according tothe phonetic transcriptions and the syllable acoustic lexicon 420, theprocessing unit 110 may obtain the phonetic spellings matching thephonetic transcription and the intonation information corresponding tothe phonetic transcriptions. The phonetic spellings and thecorresponding intonation information may partly include the phoneticspelling matching probabilities for “Nán”, “Jīng”, “Shì”, “Cháng”,“Jiāng”, “Dà”, “Qiáo”, or partly include the phonetic spelling matchingprobabilities for “Nán”, “Jīng”, “Shì”, “Zh{hacek over (a)}ng”, “Jiāng”,“Dà” “Qiáo”. Herein, it is assumed that higher phonetic spellingmatching probabilities are provided when the phonetic transcriptions(“nán”, “jīng”, “shì”, “cháng”, “jiāng”, “dà”m, “qiáo”) arecorresponding to the phonetic spellings (“Nán”, “Jīng”, “Shì”, “Cháng”,“Jiāng”, “Dà”, “Qiáo”).

Thereafter, the processing unit 110 may obtain a plurality of phoneticspelling sequences and a plurality of phonetic spelling sequenceprobabilities from the language model 230 according to the phoneticspellings (“Nán”, “Jīng”, “Shì”, “Cháng”, “Jiāng”, “Dà”, “Qiáo”, and thephonetic spellings “Nán”, “Jīng”, “Shì”, “Zh{hacek over (a)}ng”,“Jiāng”, “Dà”, “Qiáo”. In this case, it is assumed that the “Cháng”,“Jiāng”, “Dà”, “Qiáo” match the phonetic spelling sequence“Cháng-Jiāng-Dà-Qiáo” of the keyword “

” in the language model 430, so that the phonetic spelling sequenceprobability for “Nán-Jīng-Shi-Cháng-Jiāng-Dà-Qiáo” is relatively higher.Accordingly, the processing unit 110 may use“Nán-Jīng-Shì-Cháng-Jiāng-Dà-Qiáo” as the phonetic spelling sequence foroutput.

Based on above, in the speech recognition method and the electronicapparatus of the present embodiment, the electronic apparatus may buildthe acoustic model, the syllable acoustic lexicon, and the languagemodel by training with the speech signal based on different languages,dialects or different pronunciation habits. Therefore, when the speechrecognition is performed on the speech signal, the electronic apparatusmay obtain the phonetic transcriptions matching real pronunciationsaccording to the acoustic model, and obtain the phonetic spellingsmatching the phonetic transcriptions from the syllable acoustic lexicon.In particular, since the syllable acoustic lexicon includes theintonation information of each of the phonetic spellings in differentsemantemes, the electronic apparatus is capable of obtaining thephonetic spelling sequence matching the phonetic spelling and thephonetic spelling sequence probabilities thereof according to theintonation information. Accordingly, the electronic apparatus may selectthe phonetic spelling sequence corresponding to the largest one amongthe phonetic spelling sequence probabilities as the recognition resultof the speech signal.

As a result, the invention may perform decoding in the acoustic model,the syllable acoustic lexicon, and the language model according to thespeech inputs of different languages, dialects or pronunciation habits.Further, besides that a decoding result may be outputted according tothe phonetic spelling corresponding to the phonetic transcription, thephonetic spelling matching probabilities of the phonetic transcriptionmatching the phonetic spelling under different languages, dialects orpronunciation habits as well as the phonetic spelling sequenceprobabilities of each of the phonetic spellings in different phoneticspelling sequences may also be obtained. Lastly, the invention mayselect the largest one among said probabilities to be outputted as therecognition result of the speech signal. In comparison with traditionalmethods, the invention is capable of obtaining the phonetic spellingsequence corresponding to the real pronunciations of the speech input,hence the message inputted by the original speech input (e.g., apolyphone in different pronunciations) may be retained. Moreover, theinvention is also capable of converting the real pronunciations of thespeech input into the corresponding phonetic spelling sequence accordingto types of different languages, dialects or pronunciation habits. Thismay facilitate in subsequent machine speech conversations, such asdirect answer in Cantonese (or other dialects/languages) for inputspronounced in Cantonese (or other dialects/languages). In addition, theinvention may also differentiate meanings of each of the phoneticspellings according to the intonation information of the realpronunciations, so that the recognition result of the speech signal maybe more close to the meaning corresponding to the speech signal.Accordingly, the speech recognition method and the electronic apparatusof the invention may be more accurate in recognizing the language andthe semanteme corresponding to the speech signal of different languages,dialects or different pronunciation habits, so as to improve theaccuracy of the speech recognition.

It should be noted that, during the process of obtaining the phoneticspelling sequences and the phonetic spelling sequence probabilities bythe processing unit 110 through the language model 430, a probabilityfor one specific phonetic spelling mapping to the phonetic spellingsequence of one specific vocabulary is obtained, which is hereby definedas a positive probability. The speech recognition method of the presentembodiment may also achieve a higher speech recognition accuracy byusing a negative probability, so as to further determine different textsrespectively corresponding to the same pronunciation. In other words,the processing unit 110 may further process through the language model430 for mapping the phonetic spelling to the corresponding text.Therein, the processing unit 110 may search a probability of the each ofthe words matching possible pronunciations of the said words in each ofcandidate sentences, so as to obtain a plurality of texts matching thephonetic spellings and a probability of the phonetic spelling matchingeach of the texts. Lastly, a largest one among said probabilities isused as the recognition result of the speech signal (i.e., the textsequence corresponding to the speech signal). Accordingly, forpronunciations changed based on the homonyms or different habits (i.e.,pronunciations from different dialects), the speech recognition methodof the present embodiment may obtain the texts corresponding to thosepronunciations more accurately, thereby significantly improve theaccuracy in recognition In order to make the invention morecomprehensible, embodiments are described below as the examples to provethat the invention can actually be realized. Hereinafter, the speechrecognition method of the invention is still described below withreference to said electronic apparatus 100 depicted in FIG. 1 and saidspeech recognition module 400.

The present embodiment is similar to the foregoing embodiment, adifference between the two is that: The processing unit 110 of thepresent embodiment may obtain a plurality of text sequence probabilitiesand a plurality of text sequences from the speech signal S1 received bythe input unit 130 by utilizing the acoustic model 410, the syllableacoustic lexicon 420 and the language model 430. Moreover, theprocessing unit 110 may select the text sequence corresponding to alargest one among the text sequence probabilities as a recognitionresult S2 of the speech signal.

FIG. 6 is a flowchart illustrating the speech recognition methodaccording to an embodiment of the invention. Referring to FIG. 1, FIG. 4and FIG. 6 together, in step S610, the input unit 130 receives a speechsignal S1, and the speech signal S1 is, for example, a speech input froma user. In step S620, the processing unit 110 may obtain a plurality ofphonetic transcriptions of the speech signal S1 according to theacoustic model 410, and the phonetic transcriptions include a pluralityof phones. In step S630, the processing unit 110 may obtain a pluralityof phonetic spellings matching the phonetic transcriptions according toeach of the phonetic transcriptions and the syllable acoustic lexicon420. Above-said step S610 to S630 are similar to steps S510 to S530,thus related description may refer to the description of relatingparagraphs.

In step S640, the processing unit 110 may obtain a plurality of textsequences and a plurality of text sequence probabilities from thelanguage model 430 according to each of the phonetic spellings. Herein,the processing unit 110 may match each of the phonetic spelling with thecandidate sentences. A candidate sentence table records the candidatesentences, the phonetic spellings matching each of words in thecandidate sentences, and a word probability for each of the words andeach of the phonetic spellings. Therefore, the processing unit 110 mayobtain the word probability of each of the phonetic spellingscorresponding to each of the words in the candidate sentences throughthe candidate sentence table. Further, the processing unit 110 maycalculate the word probabilities of the phonetic spellings so as toobtain the text sequence probabilities, and the candidate sentencescorresponding to the text sequence probabilities are the text sequences.

More specifically, the processing unit 110 may generate the candidatesentence table in advance, so as to build the language model 430.Therein, the processing unit 110 may receive a plurality of candidatesentences, and obtain a plurality of phonetic spellings matching each ofthe words in each of the candidate sentences and a plurality of wordprobabilities according to the text corpus 42, so as to obtain thecandidate sentence table corresponding to the candidate sentences. Morespecifically, the processing unit 110 may receive a plurality of speechsignals through the input unit 130, and obtain the text corpus 42through training with the speech signals based on different languages,dialects or pronunciation habits. Herein, the processing unit 110 mayreceive the phonetic spellings matching each of the words according tothe corresponding words in the speech signals for training, therebytraining according to each of the words and the phonetic spellings forobtaining the word probabilities of each of the words corresponding toeach of the phonetic spellings in the text corpus 42.

For instance, the processing unit 110 may receive the speech signal “

” outputted in different languages, dialects or different pronunciationhabits from the input unit 130. Pronunciations based on differentlanguages, dialects or different pronunciation habits are different fromone another. Therefore, the pronunciations corresponding to the speechsignal “

” may include the phonetic spellings of “n{hacek over (i)}-h{hacek over(a)}o” (corresponding to the pronunciation of the northern), “n{hacekover (e)}i-h{hacek over (o)}u” or “nhij-ho” (corresponding to thepronunciation of a southern), or other different phonetic spellings. Inthis case, the processing unit 110 may calculate possible phoneticspellings corresponding to “

” and “

” and the corresponding probabilities thereof (i.e., the wordprobabilities) by training according to the speech signal “

” and the corresponding phonetic spellings “n{hacek over (i)}-h{hacekover (a)}o”, “n{hacek over (e)}i-h{hacek over (o)}u” or “nhij-ho”. Takethe pronunciation of the northern as an example, the word probabilitiesof the speech signal “

” corresponding to “n{hacek over (i)}”, “n{hacek over (e)}i” and “nhij”are sequentially 90%, 8% and 2%; whereas the word probabilities of thespeech signal “

” corresponding to “h{hacek over (a)}o”, “h{hacek over (o)}u” and “ho”are sequentially 82%, 10% and 8%. In other words, the candidate sentencetable obtained by the processing unit 110 records a relation betweeneach of the words and their possible pronunciations. Therein, each ofthe possible pronunciations may be annotated by the phonetic spellings,and the word probability is higher when each of the words has a higherchance of corresponding to one specific phonetic spelling. Accordingly,in the language model 430, the processing unit 110 may obtain thepossible word of the specific phonetic spelling according to each of thephonetic spellings.

Furthermore, the processing unit 110 may calculate the wordprobabilities corresponding to the phonetic spellings by multiplying theword probabilities corresponding to each of the phonetic spellings inthe speech signal S1 together, so as to obtain a product of theprobabilities corresponding to the speech signal S1 to be used as thetext sequence probabilities, and obtain the candidate sentencesconstituted by the words corresponding to the phonetic spellings to beused as the text sequence. For instance, it is assumed that the phoneticspelling obtained from the syllable acoustic lexicon 420 is “n{hacekover (e)}i-h{hacek over (o)}u”. Next, according to the candidatesentence table, the processing unit 110 may have the phonetic spelling“n{hacek over (e)}i” corresponding to the word “

”, and the phonetic spelling “h{hacek over (o)}u” corresponding to theword “

”. Further, the processing unit 110 may multiple the word probabilitiescorresponding to “n{hacek over (e)}i” and the word probabilitiescorresponding to “h{hacek over (o)}u” together, so as to obtain thecandidate sentences corresponding to “

” as the text sequence, and the text sequence probabilities at the time.Of course, the processing unit 110 may also have the phonetic spellingsof “n{hacek over (e)}i-h{hacek over (o)}u” corresponding to other words(e.g., corresponding to the text sequence “

”), so as to obtain other text sequence probabilities (e.g.,probabilities for corresponding to the text sequence “

”).

In step S650, the processing unit 110 may select the text sequencecorresponding to a largest one among the text sequence probabilities asa recognition result S2 of the speech signal. For instance, it isassumed that, for the phonetic spellings “n{hacek over (e)}i-h{hacekover (o)}u”, the text sequence probability corresponding to the textsequence “

” obtained by the processing unit 110 is P1; the text sequenceprobability corresponding to the text sequence “

” obtained by the processing unit 110 is P2; and the text sequenceprobability corresponding to the text sequence “

” obtained by the processing unit 110 is P3. Therein, P1 is greater thanP2, and P1 is greater than P3. Accordingly, the processing unit 110 mayselect the text sequence “

” corresponding to the text sequence probability P1 as the recognitionresult S2. Of course, the processing unit 110 may also display the textsequence corresponding to the largest one among the text sequenceprobabilities or other possible text sequences through the output unit140, so that the user may intuitively recognize the recognition resultS2.

It should be noted that, in the step of obtaining the text sequence andcalculating the corresponding text sequence probabilities, theprocessing unit 110 may also select a result with a higher probability(i.e., each of the phonetic transcriptions and the phonetictranscription matching probabilities obtained by the processing unit 110from the acoustic model 410, each of the phonetic spellings and thephonetic spelling matching probabilities obtained by the processing unit110 from the syllable acoustic lexicon 420, and each of the wordscorresponding to each of the phonetic spellings and the wordprobabilities obtained by the processing unit 110 from the languagemodel 430) according to different threshold values for calculations indifferent models. More specifically, according to a first thresholdvalue, the processing unit 110 may select the phonetic transcriptionhaving the phonetic transcription matching probability being greaterthan the first threshold value, and generate the corresponding phoneticspelling according to the phonetic transcriptions in the syllableacoustic lexicon 420. Meanwhile, according to a second threshold value,the processing unit 110 may select the phonetic spelling having thephonetic spelling matching probability being greater than the secondthreshold value, and generate the corresponding text sequence and thetext sequence probabilities according to the phonetic spellings in thesyllable acoustic lexicon 430. In other words, the text sequenceprobabilities calculated by the processing unit 110 is a product of thephonetic transcription matching probabilities, the phonetic spellingmatching probabilities, and the word probabilities. Therefore, theprocessing unit 110 may select the text sequence corresponding to thelargest one among associated probabilities including the phonetictranscription matching probabilities, the phonetic spelling matchingprobabilities, and the word probabilities, to be used as the recognitionresult S2 of the speech signal. Accordingly, the speech recognitionmethod of the present embodiment is capable of accurately obtaining thetext sequence of one specific pronunciation according to thepronunciation of the user, so as to eliminate a lot of ambiguityproduced while mapping the speech to the text thereby significantlyimprove the accuracy in the speech recognition.

In view of above, in the method for building the language model, speechrecognition method and the electronic apparatus of the presentembodiment, when the speech recognition is performed on the speechsignal, the electronic apparatus may obtain the phonetic transcriptionsmatching the real pronunciations according to the acoustic model, andobtain the phonetic spellings matching the phonetic transcriptions fromthe syllable acoustic lexicon. In particular, according to each of thephonetic spellings, the electronic apparatus may find the words matchingthe phonetic spelling and the word probabilities thereof from thelanguage model. Lastly, the electronic apparatus may obtain the textsequence probabilities by calculating the word probabilities of thephonetic spelling corresponding to the words, and select the textsequence corresponding to the largest one among the text sequenceprobabilities to be used as the recognition result. In comparison withtraditional methods, the invention is capable of recognizing thephonetic spelling to the text according to the phonetic spellingcorresponding to the real pronunciations of the speech inputs, hence theambiguity produced while mapping the speech to the text may beeliminated, and the message inputted by the original speech input (e.g.,a polyphone in different pronunciations) may be retained. Accordingly,the method for building the language model, the speech recognitionmethod and the electronic apparatus of the invention may be moreaccurate in recognizing the language and the semanteme corresponding tothe speech signal of different languages, dialects or differentpronunciation habits, so as to improve the accuracy of the speechrecognition.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the structure of the presentdisclosure without departing from the scope or spirit of the disclosure.In view of the foregoing, it is intended that the present disclosurecover modifications and variations of this disclosure provided they fallwithin the scope of the following claims and their equivalents.

What is claimed is:
 1. A speech recognition method, adapted to anelectronic apparatus, comprising: obtaining a phonetic transcriptionsequence of a speech signal according to an acoustic model, and thephonetic transcription sequence including a plurality of phones;obtaining a plurality of phonetic spellings matching the phonetictranscription sequence according to the phonetic transcription sequenceand a syllable acoustic lexicon; obtaining a plurality of text sequencesand a plurality of text sequence probabilities from a language modelaccording to the phonetic spellings, and matching each of the phoneticspellings with a candidate sentence table, so as to obtain a wordprobability of each of the phonetic spellings corresponding to each ofthe words in the candidate sentences; and calculating the wordprobabilities of the phonetic spellings, so as to obtain the textsequence probabilities, wherein the candidate sentences corresponding tothe text sequence probabilities are the text sequences; and selectingthe text sequence corresponding to a largest one among the text sequenceprobabilities as a recognition result of the speech signal.
 2. Thespeech recognition method of claim 1, further comprising: obtaining theacoustic model through training with the speech signals based ondifferent languages, dialects or different pronunciation habits.
 3. Thespeech recognition method of claim 2, wherein the step of obtaining theacoustic model through training with the speech signals based ondifferent languages, dialects or different pronunciation habitscomprises: receiving the phonetic transcription sequences matchingpronunciations in the speech signals; and obtaining data of the phonescorresponding to the phonetic transcription sequences in the acousticmodel by training according to the speech signals and the phonetictranscription sequences.
 4. The speech recognition method of claim 1,wherein the step of obtaining the phonetic transcription sequence of thespeech signal according to the acoustic model comprises: selecting atraining data from the acoustic model according to a predeterminedsetting, wherein the training data is one of training results ofdifferent languages, dialects or different pronunciation habits;calculating a phonetic transcription matching probability of thephonetic transcription sequences matching the phones according to theselected training data and each of the phones of the speech signal; andselecting the phonetic transcription sequence corresponding to a largestone among the phonetic transcription matching probabilities to be usedas the phonetic transcription sequence of the speech signal.
 5. Thespeech recognition method of claim 1, wherein the step of obtaining thephonetic spellings matching the phonetic transcription sequenceaccording to the phonetic transcription sequence and the syllableacoustic lexicon comprises: obtaining an intonation informationcorresponding to each of the phonetic spellings according to a tone ofthe phonetic transcription sequence.
 6. The speech recognition method ofclaim 1, wherein the step of obtaining the phonetic spellings matchingthe phonetic transcription sequence according to the phonetictranscription sequence and the syllable acoustic lexicon furthercomprises: obtaining the phonetic spellings matching the phonetictranscription sequence and obtaining a phonetic spelling matchingprobability of the phonetic transcription sequence matching each of thephonetic spellings according to the phonetic transcription sequence andthe syllable acoustic lexicon; and selecting the phonetic spellingcorresponding to a largest one among the phonetic spelling matchingprobabilities to be used as the phonetic spelling matching each of thephonetic transcription sequences.
 7. The speech recognition method ofclaim 6, further comprising: selecting the text sequence correspondingto the largest one among associated probabilities including the phoneticspelling matching probabilities and the text sequence probabilities, tobe used as the recognition result of the speech signal.
 8. The speechrecognition method of claim 1, further comprising: receiving a pluralityof candidate sentences; and obtaining a plurality of phonetic spellingsmatching each of words in each of candidate sentences and a plurality ofword probabilities according to a text corpus, so as to obtain thecandidate sentence table corresponding to the candidate sentences. 9.The speech recognition method of claim 8, further comprising: obtainingthe text corpus through training with a plurality of speech signalsbased on the speech signals of different languages, dialects ordifferent pronunciation habits.
 10. The speech recognition method ofclaim 9, wherein the step of obtaining the text corpus through trainingwith the speech signals based on different languages, dialects ordifferent pronunciation habits comprises: receiving the phoneticspellings matching pronunciations of each of the words according to thecorresponding words in the speech signals; and obtaining the wordprobabilities of each of the words corresponding to each of the phoneticspellings in the text corpus by training according to each of the wordsand the phonetic spellings.
 11. The speech recognition method of claim9, wherein the step of obtaining the text sequences and the textsequence probabilities from the language model according to the phoneticspellings comprises: selecting the candidate sentence table according toa predetermined setting, wherein the candidate sentence table iscorresponding to the text corpus obtained by training with one of thespeech signals based on different languages, dialects or differentpronunciation habits.
 12. An electronic apparatus, comprising: an inputunit, receiving a speech signal; a storage unit, storing a plurality ofprogram code segments; and a processing unit, coupled to the input unitand the storage unit, the processing unit executing a plurality ofcommands through the program code segments, and the commands comprising:obtaining a phonetic transcription sequence of the speech signalaccording to an acoustic model, and the phonetic transcription sequenceincluding a plurality of phones; obtaining a plurality of phoneticspellings matching the phonetic transcription sequence according to thephonetic transcription sequence and a syllable acoustic lexicon;obtaining a plurality of text sequences and a plurality of text sequenceprobabilities from a language model according to the phonetic spellings,and matching each of the phonetic spellings with a candidate sentencetable, so as to obtain a word probability of each of the phoneticspellings corresponding to each of the words in the candidate sentences;and calculating the word probabilities of the phonetic spellings, so asto obtain the text sequence probabilities, wherein the candidatesentences corresponding to the text sequence probabilities are the textsequences; and selecting the text sequence corresponding to a largestone among the text sequence probabilities as a recognition result of thespeech signal.
 13. The electronic apparatus of claim 12, wherein thecommands further comprise: obtaining the acoustic model through trainingwith the speech signals based on different languages, dialects ordifferent pronunciation habits.
 14. The electronic apparatus of claim13, wherein the command of obtaining the acoustic model through trainingwith the speech signals based on different languages, dialects ordifferent pronunciation habits comprises: receiving the phonetictranscription sequences matching pronunciations in the speech signals;and obtaining data of the phones corresponding to the phonetictranscription sequences in the acoustic model by training according tothe speech signals and the phonetic transcription sequences.
 15. Theelectronic apparatus of claim 12, wherein the command of obtaining thephonetic transcription sequences of the speech signal according to theacoustic model comprises: selecting a training data from the acousticmodel according to a predetermined setting, wherein the training data isone of training results of different languages, dialects or differentpronunciation habits; calculating a phonetic transcription matchingprobability of the phonetic transcription sequences matching the phonesaccording to the selected training data and each of the phones of thespeech signal; and selecting the phonetic transcription sequencecorresponding to a largest one among the phonetic transcription matchingprobabilities to be used as the phonetic transcription sequence of thespeech signal.
 16. The electronic apparatus of claim 12, wherein thecommand of obtaining the phonetic spellings matching the phonetictranscription sequence according to the phonetic transcription sequenceand the syllable acoustic lexicon comprises: obtaining an intonationinformation corresponding to each of the phonetic spellings according toa tone of the phonetic transcription sequence.
 17. The electronicapparatus of claim 12, wherein the command of obtaining the phoneticspellings matching the phonetic transcription sequence according to thephonetic transcription sequence and the syllable acoustic lexiconfurther comprises: obtaining the phonetic spellings matching thephonetic transcription sequence and obtaining a phonetic spellingmatching probability of the phonetic transcription sequence matchingeach of the phonetic spellings according to the phonetic transcriptionsequence and the syllable acoustic lexicon; and selecting the phoneticspelling corresponding to a largest one among the phonetic spellingmatching probabilities to be used as the phonetic spelling matching eachof the phonetic transcription sequences.
 18. The electronic apparatus ofclaim 17, wherein the commands further comprise: selecting the textsequence corresponding to the largest one among associated probabilitiesincluding the phonetic spelling matching probabilities and the textsequence probabilities, to be used as the recognition result of thespeech signal.
 19. The electronic apparatus of claim 12, wherein thecommands further comprise: receiving a plurality of candidate sentences;and obtaining a plurality of phonetic spellings matching each of wordsin each of candidate sentences and a plurality of word probabilitiesaccording to a text corpus, so as to obtain the candidate sentence tablecorresponding to the candidate sentences.
 20. The electronic apparatusof claim 19, wherein the commands further comprise: obtaining the textcorpus through training with a plurality of speech signals based on thespeech signals of different languages, dialects or differentpronunciation habits.
 21. The electronic apparatus of claim 20, whereinthe command of obtaining the text corpus through training with thespeech signals based on different languages, dialects or differentpronunciation habits comprises: receiving the phonetic spellingsmatching pronunciations of each of the words according to thecorresponding words in the speech signals; and obtaining the wordprobabilities of each of the words corresponding to each of the phoneticspellings in the text corpus by training according to each of the wordsand the phonetic spellings.
 22. The electronic apparatus of claim 20,wherein the command of obtaining the text sequences and the textsequence probabilities from the language model according to the phoneticspellings comprises: selecting the candidate sentence table according toa predetermined setting, wherein the candidate sentence table iscorresponding to the text corpus obtained by training with one of thespeech signals based on different languages, dialects or differentpronunciation habits.